There have been several attempts in blind SR [1]} or real-world SR.
[2]}, [3]} used a separate degradation estimation network to guide the blind SR process. Such methods usually consider simple synthetic degradations, and the accuracy of degradation representation largely affects the restoration quality. [4]}, [5]} capture real-world training pairs through specific cameras followed by tedious alignments to solve real-world SR. However, it is expensive to get such real-world training pairs, and it only works with specific imaging devices. [6]}, [7]} directly learned from unpaired data with cycle consistency loss. Nevertheless, learning fine-grained degradations with unpaired data is challenging, and the results are usually unsatisfactory. [8]}, [9]}, [10]}, [11]} generated training pairs as close to real data as possible, including various degradation factors and random/high-order degradation processes.
